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At least 451 records · Page 25

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NASA NTRS2018

Complexity-Based Link Assignment for NASA’s Deep Space Network for Follow-the-Sun Operations

NASA’s Deep Space Network (DSN) recently underwent a paradigm shift in its operations approach called Follow the Sun Operations (FtSO) in an effort to increase efficiency for forthcoming expansion of the network. This change requires each Deep Space Communications Complex (DSCC) to remotely control the other two complexes’ antennas during their local day shift, in contrast to locally controlling only their own antennas 24x7. Remote operations increases the workload of each complex during their day shift, specifically that of the Link Control Operators (LCOs), and presents a new challenge for planning and managing the distribution of responsibility for each link. A new DSN software assembly, the Link Complexity and Maintenance (LCM) software, was developed to support workload management for LCOs, as well as for planning site-local maintenance activities. The LCM deployment was a vital part of the transition to FtSO in November 2017. This paper discusses the architecture of LCM, its feature set, and lessons learned during its development and rollout.

Lee, Carlyn↗

DOE OSTI2023

Virtualizing Industrial Control Networks for Cyber Resilience Experiments

Industrial control systems (ICS) networks are undergoing constant shifts to accommodate new security measures. It is challenging to test varying network configurations and security tools with physical systems as they typically include large, expensive equipment. Not only this, but researchers often do not have access to this type of equipment for development of new security tools and techniques. As a solution to these issues, this work presents a set of tools for utilizing GNS3 and Docker as a virtual ICS network. Additionally, the virtual network can be attached to physical devices including network switches, hardware simulations, and intelligent electronic devices (IEDs). Two case studies showcase a relatively complex automatically generated network and an attack on a simple ICS network with an example mitigation.

42 ENGINEERING↗

NASA NTRS2000

A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

Kerczewski, Robert J.↗

NASA NTRS1984

Mental Models of Invisible Logical Networks

Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.

Sanderson, P.↗

DOE OSTI2022

Generative Design for Resilience of Interdependent Network Systems

Abstract Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria while the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on synthetic supply chain networks and power systems from the IEEE dataset have illustrated the applicability of the developed method for designing resilient interdependent network systems.

Engineering↗

DOE OSTI2026

Sequential Fracture Activation and Stress Evolution During EGS Stimulation at Utah FORGE Revealed by Waveform Cross‐Correlation

Mapping fracture networks in Enhanced Geothermal Systems (EGS) is essential for optimizing reservoir performance, yet complex fracture evolution during stimulation remains difficult to resolve. This study examines the evolution of microseismicity and fracture networks during stage 3 of the 2022 EGS stimulation at the Utah Frontier Observatory for Research in Geothermal Energy site. We map the fracture network represented by 20 clusters of seismic events identified by waveform similarities with cross-correlation. We characterize their geometric properties such as strike, dip, length, and width, and analyze the time evolution of activated fractures. The results reveal a systematic fracture evolution: early activation of pre-existing natural fractures, complex network development during peak injection, and continued activation of less favorably oriented fractures post-injection. Magnitude calibration using the Principal Component Analysis of cross-correlated waveforms improves relative amplitude measurements, refining estimations of the Gutenberg-Richter b-values with spatial variations in b-values suggesting stress re-distribution across the stimulated area. Analysis of the stress state of selected fractures further shows that fractures requiring higher excess pore pressure primarily activate at the end of injection and post-injection, highlighting stress transfer due to pore pressure as a dominant triggering mechanism. These findings provide insights into fracture propagation, stress evolution, and seismic hazard assessment in EGS reservoirs.

Asirifi, Richard [Texas A & M Univ., College Stati↗

NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Recognition↗

NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

Signal Classification↗

NASA NTRS1990

A neural network simulation package in CLIPS

The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

Bhatnagar, Himanshu↗

NASA NTRS2015

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

NASA plans to send humans to Mars in about 20 years. The NASA Human Research Program supports research to mitigate the major risks to human health and performance on extended missions. However, there will undoubtedly be unforeseen events on any mission of this nature - thus mitigation of known risks alone is not sufficient to ensure optimal crew health and performance. Research should be directed not only to mitigating known risks, but also to providing crews with the tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory and network theory to assess crew and individual resilience. The entire crew or the individual crewmember can be viewed as a complex system that is composed of subsystems (individual crewmembers or physiological subsystems), and the interactions between subsystems are of crucial importance for overall health and performance. An understanding of the structure of the interactions can provide important information even in the absence of complete information on the component subsystems. This is critical in human spaceflight, since insufficient flight opportunities exist to elucidate the details of each subsystem. Enabled by recent advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and also during preflight training to establish baseline values and ranges. Coupled with appropriate mathematical modeling, this can provide real-time assessment of health and function, and detect early indications of imminent breakdown. Since the interconnected web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). There are many parameters and interactions to choose from. Normal variability is an established characteristic of a healthy physiological response. Healthy coupling has been investigated less extensively, but there are cases in which too tight or too loose coupling can be problematic. This might be in inter-individual behaviors, such as sleep cycles, coordination of work and meal times, and coupled motions during communication. Less apparent are couplings of physiological systems, nevertheless examples abound of coupled systems which might be monitored: cardio-respiratory rhythms; circadian rhythms, body temperature, and sleep; stress markers and cognition, sleep, and performance; profiles of biochemical markers related to immune function and nutritional status; sensorimotor aspects such as motion sickness, ataxia, reaction time, and manual control. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into appropriate models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits. What to do when a problem is identified depends on its nature. Changes can be made to crew procedures, work pacing, interpersonal interactions, sleep cycles, meal timing and content, as guided by the model. Use and continued development of these methods could not only provide tools for resilience, but also meaningful autonomous work for the crew on an extended flight.

Shelhamer, Mark↗

DOE OSTI2024

Modeling Distributed Computing Infrastructures for HEP Applications

Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.

Horzela, Maximilian↗

DOE OSTI2025

Graph-Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical Power Networks

With the increasing complexity and data availability in modern power systems, learning-based approaches to AC Optimal Power Flow (AC OPF) have garnered significant attention. In particular, the structure of smart grids lends itself naturally to graph-based representations, where Graph Neural Networks (GNNs) can capture spatial and relational dependencies. This paper investigates attention-based GNN architectures tailored to heterogeneous graph representations of electric grids. We evaluate two major paradigms: relational attention, which distinguishes between edge types during message passing, and meta-path attention, which captures high-level semantics through multi-hop, typed paths. Using a large corpus of public AC OPF scenarios, we benchmark representative models of each type of attention. Our results demonstrate the benefits of heterogeneous attention-based models in accurately capturing grid dynamics; heterogeneous attention models achieve superior performance in both standard and perturbed settings. The findings highlight the importance of semantic-aware architectures for improving prediction robustness and interpretability in power system applications.

Trigui, Ali [Qubit Engineering Inc.]↗

NASA NTRS1994

Environmental projects. Volume 16: Waste minimization assessment

The Goldstone Deep Space Communications Complex (GDSCC), located in the MoJave Desert, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network (DSN), the world's largest and most sensitive scientific telecommunications and radio navigation network. The Goldstone Complex is operated for NASA by the Jet Propulsion Laboratory. At present, activities at the GDSCC support the operation of nine parabolic dish antennas situated at five separate locations known as 'sites.' Each of the five sites at the GDSCC has one or more antennas, called 'Deep Space Stations' (DSS's). In the course of operation of these DSS's, various hazardous and non-hazardous wastes are generated. In 1992, JPL retained Kleinfelder, Inc., San Diego, California, to quantify the various streams of hazardous and non-hazardous wastes generated at the GDSCC. In June 1992, Kleinfelder, Inc., submitted a report to JPL entitled 'Waste Minimization Assessment.' This present volume is a JPL-expanded version of the Kleinfelder, Inc. report. The 'Waste Minimization Assessment' report did not find any deficiencies in the various waste-management programs now practiced at the GDSCC, and it found that these programs are being carried out in accordance with environmental rules and regulations.

Source record↗

DOE OSTI2022

ELASTOMERIC MICROVASCULAR SELF-HEALING MATERIALS

Damaged elastomeric diaphragms within pneumatic controllers used in the oil and gas industry lead to an unintended release of methane. Self-healing microvascular materials capable of healing various types of damage have been fabricated. These microvascular materials are designed to replace currently available commercial diaphragms found in pneumatic controllers and provide a solution to reduce unintended methane leaks. Poly(dimethylsiloxane) (PDMS) was used as the main matrix material, with additional testing conducted on polyurethane and flexibilised epoxy materials. Microvascular networks were implemented into the elastomeric membranes to act as ves- sels to deliver healing agents to the damaged areas. The complex, interdigitated channel networks were created using a 3D printed custom compounded filament composed of polylactic acid (PLA) and tin(II) oxalate. Two-part liquid casting polymers were poured around the channels into a mould to create samples with the scaffolding of the microvascular channels intact. Hollow microvascular networks were created by placing the samples within a vacuum oven at 250◦C which causes the thermal depolymerisation of PLA into its gaseous monomers. The manufactured materials were placed in test stands and pressurized using nitrogen gas to determine their healing and mechanical behaviour. Self-healing behaviour was demonstrated using PDMS matrix materials and healing chemistries within a pressure test cell. This test cell was designed to detect any damage to a sample by record- ing an outlet pressure. Damages including puncture holes, diagonal cuts and star-shaped central iv cuts were applied to the samples and a reduction in the outlet pressure was recorded for all sam- ples. A variation in channel spacing and diameter was studied to determine the optimal design of the microvascular network for self-healing performance. The mechanical performance of elastomeric membrane materials within a diaphragmatic pressurised stand was observed. Deformations and strains around the channels and channel inter- sections at the midplane of the materials were recorded using digital image correlation. Normal stresses were calculated using Hooke’s law and the material properties of the PDMS matrix. A reduction in leak rate, implying the success of self-healing, was recorded within manu- factured diaphragm samples tested within a commercial valve. PDMS is the favourable material when compared to polyurethane and flexibilised epoxy for creating

03 NATURAL GAS↗

NASA NTRSDate not supplied

Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.

Loc Tran↗

NASA NTRS2002

Deep Space Network turbo decoder infusion: enhanced performance and lower decoder complexity

This article describes the effort to deploy turbo decoders in the Deep Space Network to service missions launching in 2003 and later, and the implications of these new capabilities for the design of future missions.

DSN turbo decoder↗

DOE OSTI2023

Elevated temperature alters microbial communities, but not decomposition rates, during 3 years of in situ peat decomposition

ABSTRACT Peatlands are large carbon sinks with primary production outpacing decomposition of organic matter. Results from the S pruce and P eatland R esponses U nder C hanging E nvironments (SPRUCE) study show net losses of organic matter and increased greenhouse gas production from peatlands in response to whole-ecosystem warming. Here, we investigated how warming and elevated CO 2 impact peat microbial communities and peat soil decomposition rates and characterized microbial communities through amplicon sequencing and compositional changes across four depth increments. Microbial diversity and community composition were significantly impacted by soil depth, temperature, and CO 2 treatment. Bacterial/archaeal α-diversity increased significantly with increasing temperature, and fungal α-diversity was lower under elevated CO 2 treatments. Trans domain microbial networks showed higher complexity of microbial communities in decomposition ladder depths from the warmed enclosures, and the number of highly connected hub taxa within the networks was positively correlated with temperature. Methanogenic hubs were identified in the networks constructed from the warmest enclosures, indicating increased importance of methanogenesis in response to warming. Microbial community responses were not however reflected in measures of peat soil decomposition, as warming and elevated CO 2 had no significant short-term effects on soil mass loss or composition. Regardless of treatment, on average only 4.5% of the original soil mass was lost after 3 years and variation between replicates was high, potentially masking treatment effects. Previous results at the SPRUCE experiment have shown warming is accelerating organic-matter decomposition and CO 2 and CH 4 production, and our results suggest these changes may be driven by warming-induced shifts in microbial communities. IMPORTANCE Microbial community changes in response to climate change drivers have the potential to alter the trajectory of important ecosystem functions. In this paper, we show that while microbial communities in peatland systems responded to manipulations of temperature and CO 2 concentrations, these changes were not associated with similar responses in peat decomposition rates over 3 years. It is unclear however from our current studies whether this functional resiliency over 3 years will continue over the longer time scales relevant to peatland ecosystem functions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Focus on monitoring and control of complex supply systems

The ongoing rapid transformation of our energy supply challenges the operation and stability of electric power grids and other supply networks. This focus issue comprises new ideas and concepts in the monitoring and control of complex networks to address these challenges.

97 MATHEMATICS AND COMPUTING↗